Bibliographic record
Abstract
My research explores the labour conditions experienced by foreign nurses employed in health care in the province of Nova Scotia, Canada, on temporary permits. I draw on ethnographic interviews to understand the nuanced ways in which foreign nurses feel welcomed in their local communities and workplaces, yet simultaneously remain subject to hostile racialized scrutiny. Nova Scotia is one of the least ethnically diverse provinces in Canada and one of the most economically impoverished. It faces a shortage of healthcare workers, exacerbated by the ongoing restructuring of the healthcare sector. These contextual factors contribute to the complicated push-pull matrix discussed by the temporary foreign nurses, who feel needed, but not wanted. This matrix cannot be dismissed as simply the racism and “backwardness” of local communities. Rather, it must be understood through a political economy focus on temporary foreign workers, restructured health care, and the normalization of a precarious labour landscape in which racialized foreign and local workers are pitted against each other. Mes travaux de recherche portent sur les conditions de travail subies par les infirmiers et les infirmières étrangers de permis temporaire employés dans les soins de santé dans la province de la Nouvelle-Écosse, Canada. Je m’appuie sur des entretiens ethnographiques afin de saisir les façons nuancées dont les infirmiers étrangers se sentent bien accueillis dans leurs collectivités, et en même temps font encore l’objet d’un contrôle racialisé hostile. La Nouvelle-Écosse est l’une des provinces les moins diversifiées sur le plan ethnique au Canada, ainsi que l’une des provinces les plus économiquement démunies. Elle est en outre aux prises avec une pénurie de travailleurs de la santé, aggravée par la restructuration en cours dans le secteur de la santé. Ces facteurs contextuels contribuent à la matrice complexe du « pousser/tirer » examinée par les infirmiers étrangers temporaires qui se sentent nécessaires, mais non désirés. On ne peut écarter cette matrice comme s’il s’agissait simplement du racisme et du « retard » des collectivités locales. Elle doit plutôt être comprise en mettant l’accent à caractère de l’économie politique sur les travailleurs étrangers temporaires, les soins de santé restructurés, et la normalisation d’un marché du travail précaire dans lequel les travailleurs racialisés, étrangers et domestiques, sont dressés les uns contre les autres.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".